Instructions to use jinaai/jina-embeddings-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-embeddings-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v3", trust_remote_code=True)# Load model directly from transformers import AutoModelForMultimodalLM model = AutoModelForMultimodalLM.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use jinaai/jina-embeddings-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
VORTEXRAG: 7-Layer RAG — Causal Drift Filtering + Context Poison Guard [paper + code + demo]
Relevant for anyone using this model as a retrieval backbone.
One limitation of all embedding-based retrieval (including this model) is that cosine similarity can't separate causal relevance from topical association. VORTEXRAG addresses this by adding a causal filtering layer on top of embedding retrieval.
Architecture: your embedding model handles the ANN search → VORTEXRAG's SDC/CPG layers filter by causal drift → FV layer verifies faithfulness post-generation.
Combined results with this retrieval approach: EM 74.8, Faithfulness 0.94 (+0.23 over standard embedding retrieval baseline).
The 11 domain presets (medical τ=0.35, legal τ=0.40, scientific τ=0.30, etc.) let you tune the causal strictness per use case.
Paper: https://doi.org/10.5281/zenodo.20579702
Code (MIT, 229 tests): https://github.com/vignesh2027/VORTEXRAG
Demo: https://huggingface.co/spaces/vigneshwar234/VORTEXRAG